Client data corresponding to a single client can be stored. A decision to proceed with a defragmentation of two or more particular core groups can be determined. A destination core group to receive client data can be identified. At least one additional core group can be initiated to manage interim data write requests from the single client. Each of the two or more particular core groups can be closed to data write requests. A defragmentation of the two or more particular core groups can be performed by merging into the destination core group. The destination core group can be availed to data read and delete requests. The two or more fragmented core groups can be reallocated for other uses. The additional core groups continue to receive read and write requests. The destination core groups further can be merged and defragmented further.
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2. The computer-implemented method of claim 1, wherein for the particular iteration, determining to proceed with defragmentation occurs in response to determining that a total quantity or size of data in the two or more particular core groups is below a predetermined threshold.
A computer-implemented method for optimizing data storage in a distributed system addresses the inefficiency of defragmentation processes that do not account for data distribution across storage nodes. The method involves analyzing data stored in multiple core groups within a distributed storage system to determine whether defragmentation is necessary. During a particular iteration of the analysis, the system evaluates whether the total quantity or size of data in two or more core groups falls below a predetermined threshold. If the threshold is not met, the system proceeds with defragmentation to reorganize the data, improving storage efficiency and performance. The method ensures that defragmentation is only triggered when necessary, reducing unnecessary computational overhead and system resource consumption. This approach is particularly useful in large-scale distributed storage environments where inefficient defragmentation can lead to significant performance degradation. The system dynamically assesses data distribution and storage conditions to make informed decisions about when to perform defragmentation, balancing storage optimization with system resource usage.
3. The computer-implemented method of claim 1, wherein for the particular iteration, the determination to proceed with defragmentation is based on a result of a simulation.
A computer-implemented method for optimizing data storage systems, particularly in managing fragmentation, involves determining whether to perform defragmentation based on a simulation. The method addresses the problem of inefficient storage utilization and performance degradation caused by fragmented data in storage systems. Fragmentation occurs when data is stored in non-contiguous blocks, leading to slower access times and reduced efficiency. The method simulates the potential outcomes of defragmentation to assess whether the benefits of performing the operation outweigh the costs, such as the time and resources required. The simulation evaluates factors like the current fragmentation level, expected performance improvements, and system workload to make an informed decision. This approach ensures that defragmentation is only performed when it provides a meaningful benefit, avoiding unnecessary operations that could disrupt system performance. The method integrates with a broader system that monitors storage conditions and triggers defragmentation processes based on predefined criteria, including the simulation results. By using simulations, the method improves decision-making in storage management, balancing performance optimization with resource efficiency.
4. The computer-implemented method of claim 1, wherein, for the particular iteration, the determination to proceed with defragmentation is based on a condition being satisfied for the two or more particular core groups that was not satisfied for the two or more particular core groups in a previous iteration.
This invention relates to computer-implemented methods for optimizing defragmentation processes in storage systems, particularly focusing on managing core groups within a storage device. The problem addressed is inefficient defragmentation, which can lead to unnecessary resource consumption and degraded performance. The invention improves upon existing defragmentation techniques by introducing a conditional approach that evaluates whether to proceed with defragmentation based on changes in core group conditions between iterations. The method involves analyzing two or more core groups within a storage system during a particular iteration of a defragmentation process. A determination is made to proceed with defragmentation only if a specific condition is satisfied for these core groups that was not satisfied in a previous iteration. This ensures that defragmentation is only performed when necessary, reducing unnecessary operations and improving system efficiency. The condition may relate to factors such as fragmentation levels, performance metrics, or other storage-related parameters. By dynamically assessing core group conditions across iterations, the method avoids redundant defragmentation cycles, conserving computational resources and minimizing disruptions to system operations. The approach is particularly useful in environments where storage optimization must balance performance and resource usage.
5. The computer-implemented method of claim 1, wherein a timestamp of each core group in the plurality of core groups represents an initial time at which at least some data corresponding to the single client and that was written to the core group was received, the computer-implemented method further comprising selecting an earliest timestamp from among those corresponding to the two or more particular core groups and wherein initiating the at least one additional core group comprises adjusting the timestamp of the at least one additional core group to match the earliest timestamp.
This invention relates to data management in distributed computing systems, specifically addressing the challenge of maintaining data consistency and synchronization across multiple storage nodes. The method involves organizing data into core groups, where each core group contains data associated with a single client. Each core group has a timestamp indicating the initial time when data for that client was first received and written to the group. The method further includes selecting two or more particular core groups and identifying the earliest timestamp among them. When initiating at least one additional core group, the method adjusts its timestamp to match this earliest timestamp. This ensures that the new core group aligns with the oldest data in the selected groups, promoting consistency in data processing and retrieval. The approach helps prevent data inconsistencies that may arise from asynchronous writes or delays in data propagation across distributed systems, particularly in scenarios where multiple nodes handle client data independently. By synchronizing timestamps, the method facilitates accurate data reconstruction and retrieval, improving reliability in distributed storage environments.
7. The computer-implemented method of claim 1, wherein determining to proceed is based on determining that a predetermined threshold amount of read-only core groups are present or that a time since each of the two or more particular core groups was initiated exceeds a predetermined threshold.
This invention relates to a computer-implemented method for managing core groups in a computing system, particularly focusing on determining when to proceed with an operation based on the state of read-only core groups or the time elapsed since their initiation. The method addresses the problem of efficiently managing system resources by ensuring operations only proceed under specific conditions, such as when a sufficient number of read-only core groups are present or when a predetermined time threshold has passed since the initiation of relevant core groups. The method involves monitoring the system to detect the presence of read-only core groups, which are groups of processing cores that do not modify data but only read it. If the number of these read-only core groups meets or exceeds a predefined threshold, the system determines it is safe to proceed with the operation. Alternatively, the system may also proceed if the time elapsed since the initiation of two or more particular core groups surpasses a predetermined time threshold. This ensures that operations are only executed when conditions are optimal, preventing conflicts or inefficiencies in resource usage. The method enhances system stability and performance by dynamically adjusting operations based on real-time conditions.
9. The system of claim 8, wherein for the particular iteration, determining to proceed with defragmentation occurs in response to determining that a total quantity or size of data in the two or more particular core groups is below a predetermined threshold.
A data storage system includes a controller that manages defragmentation operations for a storage device. The system organizes data into multiple core groups, each containing a subset of data blocks. During a defragmentation process, the controller evaluates the data distribution across these core groups. For a specific iteration of defragmentation, the controller determines whether to proceed based on whether the total quantity or size of data in two or more particular core groups falls below a predetermined threshold. If the threshold is not met, the defragmentation process may be skipped or adjusted to optimize storage efficiency. This approach helps reduce unnecessary defragmentation operations, conserving system resources and improving performance by avoiding redundant data movement when the data distribution is already sufficiently balanced. The system may also include additional logic to track data fragmentation metrics and dynamically adjust defragmentation criteria based on storage conditions.
10. The system of claim 8, wherein for the particular iteration, the determination to proceed with defragmentation is based on a result of a simulation.
A system for optimizing data storage defragmentation in a computing environment addresses the inefficiency of traditional defragmentation processes, which often disrupt system performance by executing unnecessary or suboptimal operations. The system evaluates whether to proceed with defragmentation for a particular iteration by simulating the expected outcomes of the defragmentation process. This simulation assesses factors such as data fragmentation levels, storage performance metrics, and potential improvements in read/write efficiency. By comparing the simulated results against predefined thresholds or performance criteria, the system determines whether the benefits of defragmentation justify the computational overhead. If the simulation indicates that defragmentation would not yield significant improvements, the system skips the operation, conserving system resources. This approach ensures that defragmentation is only performed when it provides meaningful benefits, reducing unnecessary disruptions and improving overall system efficiency. The system may also incorporate additional criteria, such as historical performance data or user-defined priorities, to refine the decision-making process.
11. The system of claim 8, wherein, for the particular iteration, the determination to proceed with defragmentation is based on a condition being satisfied for the two or more particular core groups that was not satisfied for the two or more particular core groups in a previous iteration.
This invention relates to a system for managing defragmentation in a computing environment, specifically addressing the challenge of efficiently determining when to perform defragmentation operations across multiple core groups in a processor or storage system. The system optimizes defragmentation by evaluating conditions across two or more core groups during an iteration and comparing these conditions to those from a previous iteration. If a specific condition is met in the current iteration that was not met in the prior iteration, the system proceeds with defragmentation. This approach ensures that defragmentation is only triggered when necessary, reducing unnecessary operations and improving system performance. The system may involve monitoring fragmentation levels, performance metrics, or other relevant criteria across the core groups to determine whether the condition is satisfied. By dynamically assessing these conditions, the system avoids redundant defragmentation cycles, conserving computational resources and extending the lifespan of storage media. The invention is particularly useful in environments where multiple core groups operate in parallel, such as multi-core processors or distributed storage systems, where efficient resource management is critical.
12. The system of claim 8, wherein a timestamp of each core group in the plurality of core groups represents an initial time at which at least some data corresponding to the single client and that was written to the core group was received, the computer-implemented method further comprising selecting an earliest timestamp from among those corresponding to the two or more particular core groups and wherein initiating the at least one additional core group comprises adjusting the timestamp of the at least one additional core group to match the earliest timestamp.
This invention relates to data storage systems, specifically managing data distribution across multiple core groups in a distributed storage environment. The problem addressed is ensuring data consistency and efficient retrieval when distributing data across multiple storage nodes or core groups, particularly when new storage groups are added or existing ones are modified. The system involves a plurality of core groups, each storing data for a single client. Each core group has a timestamp representing the initial time when data for the client was first written to it. When additional core groups are initiated, the system selects the earliest timestamp from among the existing core groups and adjusts the timestamp of the new core group to match this earliest timestamp. This ensures that all core groups share a consistent starting point for data retrieval, preventing discrepancies in data access and maintaining synchronization across the distributed storage system. The method also includes dynamically adjusting timestamps to reflect changes in data distribution, ensuring that new or modified core groups align with the existing data timeline. This approach optimizes data consistency and retrieval efficiency in distributed storage environments.
14. The system of claim 8, wherein determining to proceed is based on determining that a predetermined threshold amount of read-only core groups are present or that a time since each of the two or more particular core groups was initiated exceeds a predetermined threshold.
A system for managing data processing in a distributed computing environment addresses the challenge of efficiently handling read-only core groups to optimize performance and resource utilization. The system monitors the presence and activity of core groups, which are logical units of computation or data processing tasks. Specifically, the system determines whether to proceed with a particular operation based on two key conditions. First, it checks if a predetermined threshold number of read-only core groups are present in the system. Read-only core groups are those that do not modify data but only retrieve or analyze it, which can be processed more efficiently. Second, the system evaluates the time elapsed since each of the two or more core groups was initiated, ensuring that the operation only proceeds if this time exceeds a specified threshold. This approach prevents premature or unnecessary processing, thereby improving system efficiency and reducing resource overhead. The system dynamically adjusts its operations based on these conditions, ensuring optimal performance in distributed computing environments where multiple core groups may be active simultaneously.
16. The computer program product of claim 15, wherein for the particular iteration, determining to proceed with defragmentation occurs in response to determining that a total quantity or size of data in the two or more particular core groups is below a predetermined threshold.
A system and method for optimizing data storage in a computing environment, particularly for managing defragmentation processes in a distributed storage system. The invention addresses the inefficiency of conventional defragmentation techniques, which often trigger unnecessary operations, leading to wasted computational resources and degraded system performance. The solution involves a dynamic defragmentation decision-making process that evaluates the state of data distribution across multiple storage nodes or core groups before initiating defragmentation. The system monitors the total quantity or size of data stored in two or more core groups within the storage system. If the combined data in these groups falls below a predetermined threshold during a particular iteration of the monitoring process, the system determines that defragmentation should proceed. This threshold-based approach ensures that defragmentation is only performed when necessary, reducing unnecessary computational overhead and improving overall system efficiency. The method may also involve additional criteria, such as evaluating data fragmentation levels or system performance metrics, to further refine the decision-making process. By dynamically assessing storage conditions, the invention optimizes resource utilization and maintains system performance without unnecessary disruptions.
17. The computer program product of claim 15, wherein for the particular iteration, the determination to proceed with defragmentation is based on a result of a simulation.
A system and method for optimizing data storage defragmentation in a computer system. The invention addresses the problem of inefficient defragmentation processes that consume excessive computational resources and time, particularly in large-scale storage systems. The solution involves simulating the potential outcomes of a defragmentation operation before executing it, allowing the system to evaluate whether the benefits of defragmentation outweigh the costs in terms of performance and resource usage. The simulation process analyzes the current state of the storage system, including data fragmentation levels, access patterns, and system workload, to predict the impact of defragmentation. Based on this simulation, the system determines whether to proceed with the actual defragmentation or defer it to a more optimal time. This decision-making process ensures that defragmentation is only performed when it provides a meaningful improvement in storage efficiency or performance, avoiding unnecessary operations that could disrupt system performance. The invention also includes mechanisms for dynamically adjusting the simulation parameters based on historical data and real-time system conditions, improving the accuracy of the predictions over time. This adaptive approach ensures that the defragmentation process remains efficient and aligned with the evolving needs of the storage system. The overall goal is to minimize the overhead associated with defragmentation while maximizing its benefits, leading to a more efficient and reliable storage management system.
18. The computer program product of claim 15, wherein, for the particular iteration, the determination to proceed with defragmentation is based on a condition being satisfied for the two or more particular core groups that was not satisfied for the two or more particular core groups in a previous iteration.
This invention relates to computer storage systems and specifically to methods for optimizing defragmentation processes in storage devices. The problem addressed is inefficient defragmentation, which can lead to unnecessary resource consumption and degraded system performance. The invention provides a computer program product that improves defragmentation decisions by analyzing core groups of data within a storage device. The system monitors storage conditions across multiple iterations of defragmentation. For a particular iteration, the decision to proceed with defragmentation is based on whether a specific condition is met for two or more core groups that was not met in a previous iteration. This ensures that defragmentation only occurs when meaningful changes in storage conditions justify the operation, avoiding unnecessary processing. The condition may relate to factors such as fragmentation levels, data access patterns, or storage performance metrics. The invention also includes mechanisms to track and compare storage conditions between iterations, ensuring that defragmentation is triggered only when significant changes occur. This selective approach reduces computational overhead and extends the lifespan of storage devices by minimizing unnecessary write operations. The system may also prioritize defragmentation for core groups that exhibit the most significant changes, further optimizing storage performance.
19. The computer program product of claim 15, wherein a timestamp of each core group in the plurality of core groups represents an initial time at which at least some data corresponding to the single client and that was written to the core group was received, the computer-implemented method further comprising selecting an earliest timestamp from among those corresponding to the two or more particular core groups and wherein initiating the at least one additional core group comprises adjusting the timestamp of the at least one additional core group to match the earliest timestamp.
This invention relates to data management in distributed computing systems, specifically optimizing data storage and retrieval for a single client across multiple core groups. The problem addressed is ensuring data consistency and efficient access when handling large datasets distributed across multiple storage units (core groups), particularly when new storage units are added. The system involves a plurality of core groups, each storing data for a single client. Each core group has a timestamp indicating the initial time when data for the client was first written to it. When additional core groups are needed, the system selects the earliest timestamp from the existing core groups and assigns this timestamp to the new core group. This ensures that all core groups, including new ones, share a consistent starting point for data retrieval, preventing inconsistencies and improving data access efficiency. The method further includes dynamically adjusting timestamps when new core groups are initiated, ensuring alignment with the oldest data timestamp among existing groups. This approach is particularly useful in distributed databases or cloud storage systems where data is spread across multiple nodes, and maintaining consistency is critical for accurate data processing and retrieval. The invention enhances performance by reducing latency in data access and ensuring that all core groups operate with synchronized temporal references.
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September 28, 2022
April 30, 2024
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